Á¤º¸°úÇÐȸ ÄÄÇ»ÆÃÀÇ ½ÇÁ¦ ³í¹®Áö (KIISE Transactions on Computing Practices)
Current Result Document : 3 / 4
ÇѱÛÁ¦¸ñ(Korean Title) |
´ëÈ·ÂÀü ¹× ±â°èÈ º¸º´ ½Ã³ª¸®¿À¸¦ ÅëÇÑ ´ë±Ô¸ð °¡»ó±ºÀÇ POMDP Çൿ°èȹ ¹× ÇнÀ »ç·Ê¿¬±¸ |
¿µ¹®Á¦¸ñ(English Title) |
Case Studies on Planning and Learning for Large-Scale CGFs with POMDPs through Counterfire and Mechanized Infantry Scenarios |
ÀúÀÚ(Author) |
ÀÌÁ¾¹Î
È«Á¤Ç¥
¹ÚÀ翵
ÀÌ°ÈÆ
±è±âÀÀ
¹®ÀÏö
¹ÚÀçÇö
Jongmin Lee
Jungpyo Hong
Jaeyoung Park
Kanghoon Lee
Kee-Eung Kim
Il-Chul Moon
Jae-Hyun Park
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¿ø¹®¼ö·Ïó(Citation) |
VOL 23 NO. 06 PP. 0343 ~ 0349 (2017. 06) |
Çѱ۳»¿ë (Korean Abstract) |
´ë±Ô¸ð °¡»ó±ºÀÇ ÀüÅõ ¸ðµ¨¸µ ¹× ½Ã¹Ä·¹À̼ǿ¡¼ ÀÚÀ²ÀûÀ¸·Î ÇൿÇÏ´Â À̼ºÀû ÀüÅõ °³Ã¼ÀÇ Çൿ ¹¦»ç´Â ÇâÈÄ ¹ß»ýÇÒ ÀüÅõÀÇ ÀÛÀüÀ» °íµµÈÇÏ°í È¿À²ÀûÀÎ ¸ðÀÇ ÈÆ·ÃÀ» °¡´ÉÇÏ°Ô ÇÏ´Â ÇÙ½É ¿ä¼ÒÀÌ´Ù. DEVS-POMDP °èÃþÀû ÇÁ·¹ÀÓ¿öÅ©´Â ÀüÅõ Çൿ ±³¹ü¿¡ µû¸¥ »óÀ§ ´Ü°è ÀÇ»ç°áÁ¤ ¹× ±¸Ã¼Àû ¼¼úÀÌ ¾î·Á¿î ÇÏÀ§ ´Ü°è ÀÚÀ² Çൿ°èȹÀ» °¢°¢ DEVS ¹× POMDP·Î ¸ðµ¨¸µÇÔÀ¸·Î½á ´ë±Ô¸ð °¡»ó±ºÀ» ¸ðÀÇÇÏ¿´À¸³ª, POMDP ÃÖÀû ÇൿÁ¤Ã¥ °è»ê¿¡ ÀÖ¾î¼ ¸¹Àº ÄÄÇ»Æà ÀÚ¿øÀ» ÇÊ¿ä·Î ÇÏ´Â ´ÜÁ¡ÀÌ ÀÖ¾ú´Ù. º» ³í¹®¿¡¼´Â DEVS-POMDP·Î ¸ðµ¨¸µµÈ ´ëÈ·ÂÀü ¸ðÀÇ ½Ã³ª¸®¿À ¹× ±â°èÈ º¸º´¿©´Ü °ø°ÝÀÛÀü ¸ðÀÇ ½Ã³ª¸®¿ÀÀÇ »ç·Ê¿¬±¸¸¦ ÅëÇØ È¿À²ÀûÀÎ POMDP Æ®¸® Ž»ö ¾Ë°í¸®ÁòÀ» Á¦¾ÈÇÏ°í Àû±º Çൿ ¾ç»ó ¸ðµ¨ÀÇ ÇнÀÀ» ÅëÇÑ °¡»ó±º ÀüÅõ °³Ã¼ÀÇ ¼º´É Çâ»óÀ» È®ÀÎÇÑ´Ù.
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¿µ¹®³»¿ë (English Abstract) |
Combat modeling and simulation (M&S) of large-scale computer generated forces (CGFs) enables the development of even the most sophisticated strategy of combat warfare and the efficient facilitation of a comprehensive simulation of the upcoming battle. The DEVS-POMDP framework is proposed where the DEVS framework describing the explicit behavior rules in military doctrines, and POMDP model describing the autonomous behavior of the CGFs are hierarchically combined to capture the complexity of realistic world combat modeling and simulation. However, it has previously been well documented that computing the optimal policy of a POMDP model is computationally demanding. In this paper, we show that not only can the performance of CGFs be improved by an efficient POMDP tree search algorithm but CGFs are also able to conveniently learn the behavior model of the enemy through case studies in the scenario of counterfire warfare and the scenario of a mechanized infantry brigade¡¯s offensive operations.
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Å°¿öµå(Keyword) |
ÀüÅõ °³Ã¼ ¸ðµ¨¸µ
¸ðµ¨¸µ ½Ã¹Ä·¹À̼Ç
POMDP
DEVS
combat entity modeling
modeling and simulation
POMDP
DEVSverification
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